Binary Priority Outlier Classifier Based Outlier Elimination

Optimal Trimming and Outlier Elimination

Outlier Detection Using Classifier Instability

When a classiier is used to classify objects, it is important to know if these objects resemble the train objects the classiier is trained with. Several methods to detect novel objects exist. In this paper a new method is presented which is based on the instability of the output of simple classiiers on new objects. The performances of the outlier detection methods is shown in a handwritten digi...

FP-outlier: Frequent pattern based outlier detection

An outlier in a dataset is an observation or a point that is considerably dissimilar to or inconsistent with the remainder of the data. Detection of such outliers is important for many applications and has recently attracted much attention in the data mining research community. In this paper, we present a new method to detect outliers by discovering frequent patterns (or frequent itemsets) from...

Outlier Analysis Outlier Analysis

Rank-Based Outlier Detection

We propose a new approach for outlier detection, based on a new ranking measure that focuses on the question of whether a point is “important” for its nearest neighbors; using our notations low cumulative rank implies the point is central. For instance, a point centrally located in a cluster has relatively low cumulative sum of ranks because it is among the nearest neighbors of its own nearest ...